Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations1499
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory210.9 KiB
Average record size in memory144.1 B

Variable types

Numeric8
Text3
Categorical5
DateTime2

Alerts

Fuel in is highly overall correlated with Fuel outHigh correlation
Fuel out is highly overall correlated with Fuel inHigh correlation
KMs IN is highly overall correlated with KMs outHigh correlation
KMs out is highly overall correlated with KMs INHigh correlation
damage type is highly overall correlated with locationHigh correlation
delivered by is highly overall correlated with returned byHigh correlation
location is highly overall correlated with damage typeHigh correlation
returned by is highly overall correlated with delivered byHigh correlation
# is uniformly distributedUniform
# has unique valuesUnique
Fuel in has 27 (1.8%) zerosZeros
Fuel out has 25 (1.7%) zerosZeros
Fuel Diff has 1408 (93.9%) zerosZeros

Reproduction

Analysis started2024-10-24 06:37:33.441406
Analysis finished2024-10-24 06:37:56.743164
Duration23.3 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

#
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750
Minimum1
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T09:37:56.881836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile75.9
Q1375.5
median750
Q31124.5
95-th percentile1424.1
Maximum1499
Range1498
Interquartile range (IQR)749

Descriptive statistics

Standard deviation432.86834
Coefficient of variation (CV)0.57715779
Kurtosis-1.2
Mean750
Median Absolute Deviation (MAD)375
Skewness0
Sum1124250
Variance187375
MonotonicityStrictly increasing
2024-10-24T09:37:57.167969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
997 1
 
0.1%
1006 1
 
0.1%
1005 1
 
0.1%
1004 1
 
0.1%
1003 1
 
0.1%
1002 1
 
0.1%
1001 1
 
0.1%
1000 1
 
0.1%
999 1
 
0.1%
Other values (1489) 1489
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1499 1
0.1%
1498 1
0.1%
1497 1
0.1%
1496 1
0.1%
1495 1
0.1%
1494 1
0.1%
1493 1
0.1%
1492 1
0.1%
1491 1
0.1%
1490 1
0.1%

plate number
Text

Distinct337
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-10-24T09:37:57.831055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.0080053
Min length8

Characters and Unicode

Total characters12004
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)4.8%

Sample

1st row70-29280
2nd row70-26587
3rd row70-25180
4th row70-26523
5th row70-30719
ValueCountFrequency (%)
70-28946 13
 
0.9%
70-24166 13
 
0.9%
70-24837 13
 
0.9%
70-28698 13
 
0.9%
70-24118 12
 
0.8%
70-28940 12
 
0.8%
70-26840 12
 
0.8%
70-25198 12
 
0.8%
70-24549 12
 
0.8%
70-28286 11
 
0.7%
Other values (324) 1376
91.8%
2024-10-24T09:37:58.613281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 1877
15.6%
0 1752
14.6%
2 1609
13.4%
- 1500
12.5%
5 895
7.5%
3 769
6.4%
6 766
6.4%
4 765
6.4%
9 757
6.3%
8 665
 
5.5%
Other values (2) 649
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10494
87.4%
Dash Punctuation 1500
 
12.5%
Space Separator 10
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1877
17.9%
0 1752
16.7%
2 1609
15.3%
5 895
8.5%
3 769
7.3%
6 766
7.3%
4 765
7.3%
9 757
7.2%
8 665
 
6.3%
1 639
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 1500
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 1877
15.6%
0 1752
14.6%
2 1609
13.4%
- 1500
12.5%
5 895
7.5%
3 769
6.4%
6 766
6.4%
4 765
6.4%
9 757
6.3%
8 665
 
5.5%
Other values (2) 649
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 1877
15.6%
0 1752
14.6%
2 1609
13.4%
- 1500
12.5%
5 895
7.5%
3 769
6.4%
6 766
6.4%
4 765
6.4%
9 757
6.3%
8 665
 
5.5%
Other values (2) 649
 
5.4%

car
Categorical

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
FORTUNER
232 
CERATO
178 
COROLLA
153 
H 1
101 
HILUX
97 
Other values (20)
738 

Length

Max length10
Median length7
Mean length5.9239493
Min length3

Characters and Unicode

Total characters8880
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowTUCSAN
2nd rowELANTRA
3rd rowAVANZA
4th rowFLUENCE
5th rowFLUENCE

Common Values

ValueCountFrequency (%)
FORTUNER 232
15.5%
CERATO 178
11.9%
COROLLA 153
10.2%
H 1 101
 
6.7%
HILUX 97
 
6.5%
ELANTRA 87
 
5.8%
CAMRY 86
 
5.7%
SPARK 78
 
5.2%
RIO 70
 
4.7%
AVANZA 62
 
4.1%
Other values (15) 355
23.7%

Length

2024-10-24T09:37:58.942383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fortuner 232
14.1%
cerato 178
 
10.8%
corolla 156
 
9.5%
h 101
 
6.1%
1 101
 
6.1%
hilux 97
 
5.9%
elantra 87
 
5.3%
camry 86
 
5.2%
spark 78
 
4.7%
rio 70
 
4.3%
Other values (17) 459
27.9%

Most occurring characters

ValueCountFrequency (%)
R 1315
14.8%
A 1036
11.7%
O 921
10.4%
E 714
 
8.0%
T 592
 
6.7%
C 569
 
6.4%
L 551
 
6.2%
N 484
 
5.5%
U 448
 
5.0%
I 317
 
3.6%
Other values (16) 1933
21.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8538
96.1%
Decimal Number 183
 
2.1%
Space Separator 159
 
1.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1315
15.4%
A 1036
12.1%
O 921
10.8%
E 714
8.4%
T 592
 
6.9%
C 569
 
6.7%
L 551
 
6.5%
N 484
 
5.7%
U 448
 
5.2%
I 317
 
3.7%
Other values (12) 1591
18.6%
Decimal Number
ValueCountFrequency (%)
1 138
75.4%
0 37
 
20.2%
4 8
 
4.4%
Space Separator
ValueCountFrequency (%)
159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8538
96.1%
Common 342
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1315
15.4%
A 1036
12.1%
O 921
10.8%
E 714
8.4%
T 592
 
6.9%
C 569
 
6.7%
L 551
 
6.5%
N 484
 
5.7%
U 448
 
5.2%
I 317
 
3.7%
Other values (12) 1591
18.6%
Common
ValueCountFrequency (%)
159
46.5%
1 138
40.4%
0 37
 
10.8%
4 8
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1315
14.8%
A 1036
11.7%
O 921
10.4%
E 714
 
8.0%
T 592
 
6.7%
C 569
 
6.4%
L 551
 
6.2%
N 484
 
5.5%
U 448
 
5.0%
I 317
 
3.6%
Other values (16) 1933
21.8%

damage type
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
غيار زيت
594 
اصلاح مكانيك
466 
اصلاح بودي
229 
اصلاح كوشوك
124 
اصلاح كهرباء
 
46
Other values (2)
 
40

Length

Max length13
Median length12
Mean length10.271514
Min length8

Characters and Unicode

Total characters15397
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowاصلاح بودي
2nd rowاصلاح بودي
3rd rowاصلاح مكانيك
4th rowاصلاح بودي
5th rowغيار زيت

Common Values

ValueCountFrequency (%)
غيار زيت 594
39.6%
اصلاح مكانيك 466
31.1%
اصلاح بودي 229
 
15.3%
اصلاح كوشوك 124
 
8.3%
اصلاح كهرباء 46
 
3.1%
اصلاح زجاج 21
 
1.4%
اصلاح فرش 19
 
1.3%

Length

2024-10-24T09:37:59.182617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T09:37:59.483398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
اصلاح 905
30.2%
غيار 594
19.8%
زيت 594
19.8%
مكانيك 466
15.5%
بودي 229
 
7.6%
كوشوك 124
 
4.1%
كهرباء 46
 
1.5%
زجاج 21
 
0.7%
فرش 19
 
0.6%

Most occurring characters

ValueCountFrequency (%)
ا 2937
19.1%
1965
12.8%
ي 1883
12.2%
ك 1226
8.0%
ص 905
 
5.9%
ل 905
 
5.9%
ح 905
 
5.9%
ر 659
 
4.3%
ز 615
 
4.0%
غ 594
 
3.9%
Other values (11) 2803
18.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13432
87.2%
Space Separator 1965
 
12.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 2937
21.9%
ي 1883
14.0%
ك 1226
9.1%
ص 905
 
6.7%
ل 905
 
6.7%
ح 905
 
6.7%
ر 659
 
4.9%
ز 615
 
4.6%
غ 594
 
4.4%
ت 594
 
4.4%
Other values (10) 2209
16.4%
Space Separator
ValueCountFrequency (%)
1965
100.0%

Most occurring scripts

ValueCountFrequency (%)
Arabic 13432
87.2%
Common 1965
 
12.8%

Most frequent character per script

Arabic
ValueCountFrequency (%)
ا 2937
21.9%
ي 1883
14.0%
ك 1226
9.1%
ص 905
 
6.7%
ل 905
 
6.7%
ح 905
 
6.7%
ر 659
 
4.9%
ز 615
 
4.6%
غ 594
 
4.4%
ت 594
 
4.4%
Other values (10) 2209
16.4%
Common
ValueCountFrequency (%)
1965
100.0%

Most occurring blocks

ValueCountFrequency (%)
Arabic 13432
87.2%
ASCII 1965
 
12.8%

Most frequent character per block

Arabic
ValueCountFrequency (%)
ا 2937
21.9%
ي 1883
14.0%
ك 1226
9.1%
ص 905
 
6.7%
ل 905
 
6.7%
ح 905
 
6.7%
ر 659
 
4.9%
ز 615
 
4.6%
غ 594
 
4.4%
ت 594
 
4.4%
Other values (10) 2209
16.4%
ASCII
ValueCountFrequency (%)
1965
100.0%

date in
Date

Distinct301
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Minimum2015-01-01 00:00:00
Maximum2016-02-03 00:00:00
2024-10-24T09:37:59.808594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:38:00.157227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

KMs IN
Real number (ℝ)

HIGH CORRELATION 

Distinct1423
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63581.833
Minimum390
Maximum754935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T09:38:00.570312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum390
5-th percentile6823.8
Q143710.5
median65890
Q382080
95-th percentile106887.2
Maximum754935
Range754545
Interquartile range (IQR)38369.5

Descriptive statistics

Standard deviation40221.634
Coefficient of variation (CV)0.63259634
Kurtosis110.99153
Mean63581.833
Median Absolute Deviation (MAD)18579
Skewness6.9516122
Sum95309167
Variance1.6177799 × 109
MonotonicityNot monotonic
2024-10-24T09:38:01.557617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88975 3
 
0.2%
65195 2
 
0.1%
59400 2
 
0.1%
98565 2
 
0.1%
49514 2
 
0.1%
68992 2
 
0.1%
86925 2
 
0.1%
78756 2
 
0.1%
66700 2
 
0.1%
70835 2
 
0.1%
Other values (1413) 1478
98.6%
ValueCountFrequency (%)
390 1
0.1%
790 1
0.1%
1741 1
0.1%
1835 1
0.1%
1902 1
0.1%
2085 1
0.1%
2160 1
0.1%
2274 1
0.1%
2487 1
0.1%
2509 1
0.1%
ValueCountFrequency (%)
754935 1
0.1%
716952 1
0.1%
481725 1
0.1%
194840 1
0.1%
191800 1
0.1%
162300 1
0.1%
155384 1
0.1%
154459 1
0.1%
151982 1
0.1%
148130 1
0.1%

Fuel in
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30859573
Minimum0
Maximum1
Zeros27
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T09:38:01.834961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.25
Q30.38
95-th percentile0.38
Maximum1
Range1
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.11175463
Coefficient of variation (CV)0.36213927
Kurtosis10.110095
Mean0.30859573
Median Absolute Deviation (MAD)0
Skewness1.7630578
Sum462.585
Variance0.012489098
MonotonicityNot monotonic
2024-10-24T09:38:02.087891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.25 780
52.0%
0.38 583
38.9%
0.13 46
 
3.1%
0.5 32
 
2.1%
0 27
 
1.8%
0.63 9
 
0.6%
0.75 9
 
0.6%
1 8
 
0.5%
0.88 4
 
0.3%
0.125 1
 
0.1%
ValueCountFrequency (%)
0 27
 
1.8%
0.125 1
 
0.1%
0.13 46
 
3.1%
0.25 780
52.0%
0.38 583
38.9%
0.5 32
 
2.1%
0.63 9
 
0.6%
0.75 9
 
0.6%
0.88 4
 
0.3%
1 8
 
0.5%
ValueCountFrequency (%)
1 8
 
0.5%
0.88 4
 
0.3%
0.75 9
 
0.6%
0.63 9
 
0.6%
0.5 32
 
2.1%
0.38 583
38.9%
0.25 780
52.0%
0.13 46
 
3.1%
0.125 1
 
0.1%
0 27
 
1.8%

date ready
Date

Distinct320
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Minimum2015-01-01 00:00:00
Maximum2016-02-03 00:00:00
2024-10-24T09:38:02.442383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:38:02.852539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

KMs out
Real number (ℝ)

HIGH CORRELATION 

Distinct1427
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63590.732
Minimum400
Maximum754945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T09:38:03.241211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile6834.4
Q143724.5
median65897
Q382088
95-th percentile106896.2
Maximum754945
Range754545
Interquartile range (IQR)38363.5

Descriptive statistics

Standard deviation40221.529
Coefficient of variation (CV)0.63250614
Kurtosis110.99291
Mean63590.732
Median Absolute Deviation (MAD)18580
Skewness6.9516636
Sum95322507
Variance1.6177714 × 109
MonotonicityNot monotonic
2024-10-24T09:38:03.543945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5262 3
 
0.2%
88980 3
 
0.2%
63562 3
 
0.2%
50600 3
 
0.2%
53523 2
 
0.1%
51255 2
 
0.1%
77767 2
 
0.1%
69953 2
 
0.1%
94739 2
 
0.1%
94720 2
 
0.1%
Other values (1417) 1475
98.4%
ValueCountFrequency (%)
400 1
0.1%
803 1
0.1%
1749 1
0.1%
1846 1
0.1%
1911 1
0.1%
2097 1
0.1%
2165 1
0.1%
2280 1
0.1%
2500 1
0.1%
2518 1
0.1%
ValueCountFrequency (%)
754945 1
0.1%
716961 1
0.1%
481731 1
0.1%
194848 1
0.1%
191810 1
0.1%
162304 1
0.1%
155390 1
0.1%
154465 1
0.1%
151991 1
0.1%
148140 1
0.1%

KMs Diff
Real number (ℝ)

Distinct39
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8992662
Minimum0
Maximum71
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T09:38:03.866211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q16
median9
Q310
95-th percentile16
Maximum71
Range71
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.8396028
Coefficient of variation (CV)0.54382043
Kurtosis40.231497
Mean8.8992662
Median Absolute Deviation (MAD)2
Skewness4.8501891
Sum13340
Variance23.421755
MonotonicityNot monotonic
2024-10-24T09:38:04.125977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
9 367
24.5%
5 184
12.3%
8 182
12.1%
7 175
11.7%
10 149
9.9%
6 134
 
8.9%
11 60
 
4.0%
4 53
 
3.5%
12 32
 
2.1%
13 24
 
1.6%
Other values (29) 139
 
9.3%
ValueCountFrequency (%)
0 1
 
0.1%
1 1
 
0.1%
2 2
 
0.1%
3 11
 
0.7%
4 53
 
3.5%
5 184
12.3%
6 134
 
8.9%
7 175
11.7%
8 182
12.1%
9 367
24.5%
ValueCountFrequency (%)
71 1
 
0.1%
58 1
 
0.1%
55 1
 
0.1%
50 1
 
0.1%
42 1
 
0.1%
41 1
 
0.1%
38 2
0.1%
34 1
 
0.1%
32 3
0.2%
30 3
0.2%

Fuel out
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31420947
Minimum0
Maximum1
Zeros25
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T09:38:04.375000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.25
Q30.38
95-th percentile0.392
Maximum1
Range1
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.11511977
Coefficient of variation (CV)0.36637907
Kurtosis10.005328
Mean0.31420947
Median Absolute Deviation (MAD)0
Skewness1.9121434
Sum471
Variance0.013252562
MonotonicityNot monotonic
2024-10-24T09:38:04.575195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.25 769
51.3%
0.38 595
39.7%
0.5 40
 
2.7%
0.13 35
 
2.3%
0 25
 
1.7%
0.63 11
 
0.7%
0.88 9
 
0.6%
1 8
 
0.5%
0.75 7
 
0.5%
ValueCountFrequency (%)
0 25
 
1.7%
0.13 35
 
2.3%
0.25 769
51.3%
0.38 595
39.7%
0.5 40
 
2.7%
0.63 11
 
0.7%
0.75 7
 
0.5%
0.88 9
 
0.6%
1 8
 
0.5%
ValueCountFrequency (%)
1 8
 
0.5%
0.88 9
 
0.6%
0.75 7
 
0.5%
0.63 11
 
0.7%
0.5 40
 
2.7%
0.38 595
39.7%
0.25 769
51.3%
0.13 35
 
2.3%
0 25
 
1.7%

Fuel Diff
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0056137425
Minimum-0.38
Maximum0.38
Zeros1408
Zeros (%)93.9%
Negative16
Negative (%)1.1%
Memory size11.8 KiB
2024-10-24T09:38:04.784180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.38
5-th percentile0
Q10
median0
Q30
95-th percentile0.012
Maximum0.38
Range0.76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.040568271
Coefficient of variation (CV)7.2265999
Kurtosis31.636671
Mean0.0056137425
Median Absolute Deviation (MAD)0
Skewness2.2936848
Sum8.415
Variance0.0016457846
MonotonicityNot monotonic
2024-10-24T09:38:04.999023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1408
93.9%
0.13 39
 
2.6%
0.12 25
 
1.7%
-0.13 8
 
0.5%
0.25 7
 
0.5%
-0.25 4
 
0.3%
-0.12 3
 
0.2%
0.37 2
 
0.1%
-0.38 1
 
0.1%
0.38 1
 
0.1%
ValueCountFrequency (%)
-0.38 1
 
0.1%
-0.25 4
 
0.3%
-0.13 8
 
0.5%
-0.12 3
 
0.2%
0 1408
93.9%
0.12 25
 
1.7%
0.13 39
 
2.6%
0.25 7
 
0.5%
0.255 1
 
0.1%
0.37 2
 
0.1%
ValueCountFrequency (%)
0.38 1
 
0.1%
0.37 2
 
0.1%
0.255 1
 
0.1%
0.25 7
 
0.5%
0.13 39
 
2.6%
0.12 25
 
1.7%
0 1408
93.9%
-0.12 3
 
0.2%
-0.13 8
 
0.5%
-0.25 4
 
0.3%

cost
Real number (ℝ)

Distinct166
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.94663
Minimum2
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T09:38:05.269531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile20
Q121
median50
Q3102
95-th percentile361.3
Maximum2500
Range2498
Interquartile range (IQR)81

Descriptive statistics

Standard deviation166.28248
Coefficient of variation (CV)1.5404138
Kurtosis51.203579
Mean107.94663
Median Absolute Deviation (MAD)29
Skewness5.3691081
Sum161812
Variance27649.862
MonotonicityNot monotonic
2024-10-24T09:38:05.577148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 451
30.1%
281 83
 
5.5%
92 81
 
5.4%
102 75
 
5.0%
20 38
 
2.5%
50 36
 
2.4%
80 35
 
2.3%
60 31
 
2.1%
19 25
 
1.7%
25 25
 
1.7%
Other values (156) 619
41.3%
ValueCountFrequency (%)
2 3
 
0.2%
4 1
 
0.1%
5 3
 
0.2%
8 1
 
0.1%
10 4
 
0.3%
12 1
 
0.1%
13 3
 
0.2%
14 2
 
0.1%
15 23
1.5%
16 2
 
0.1%
ValueCountFrequency (%)
2500 1
0.1%
2100 1
0.1%
1500 1
0.1%
1250 1
0.1%
1150 1
0.1%
1140 1
0.1%
1110 1
0.1%
1000 2
0.1%
900 1
0.1%
800 1
0.1%

location
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
الغزاوي
680 
القسطل
202 
المركزية
149 
معاذ عليان
109 
4 جيد
81 
Other values (27)
278 

Length

Max length19
Median length18
Mean length7.3035357
Min length3

Characters and Unicode

Total characters10948
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowالمركزية
2nd rowالمركزية
3rd rowالمركزية
4th rowابو خضر
5th rowالمركزية

Common Values

ValueCountFrequency (%)
الغزاوي 680
45.4%
القسطل 202
 
13.5%
المركزية 149
 
9.9%
معاذ عليان 109
 
7.3%
4 جيد 81
 
5.4%
امجد العطاري 46
 
3.1%
شارلي 26
 
1.7%
قسطل 23
 
1.5%
ابو نعمه 22
 
1.5%
هانكونك 20
 
1.3%
Other values (22) 141
 
9.4%

Length

2024-10-24T09:38:05.862304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
الغزاوي 680
37.6%
القسطل 202
 
11.2%
المركزية 149
 
8.2%
معاذ 109
 
6.0%
عليان 109
 
6.0%
4 81
 
4.5%
جيد 81
 
4.5%
امجد 46
 
2.5%
العطاري 46
 
2.5%
ابو 30
 
1.7%
Other values (35) 277
15.3%

Most occurring characters

ValueCountFrequency (%)
ا 2314
21.1%
ل 1566
14.3%
ي 1204
11.0%
ز 886
 
8.1%
و 771
 
7.0%
غ 694
 
6.3%
م 408
 
3.7%
311
 
2.8%
ع 310
 
2.8%
ط 286
 
2.6%
Other values (20) 2198
20.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10528
96.2%
Space Separator 311
 
2.8%
Decimal Number 81
 
0.7%
Math Symbol 28
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 2314
22.0%
ل 1566
14.9%
ي 1204
11.4%
ز 886
 
8.4%
و 771
 
7.3%
غ 694
 
6.6%
م 408
 
3.9%
ع 310
 
2.9%
ط 286
 
2.7%
ر 283
 
2.7%
Other values (17) 1806
17.2%
Space Separator
ValueCountFrequency (%)
311
100.0%
Decimal Number
ValueCountFrequency (%)
4 81
100.0%
Math Symbol
ValueCountFrequency (%)
+ 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Arabic 10528
96.2%
Common 420
 
3.8%

Most frequent character per script

Arabic
ValueCountFrequency (%)
ا 2314
22.0%
ل 1566
14.9%
ي 1204
11.4%
ز 886
 
8.4%
و 771
 
7.3%
غ 694
 
6.6%
م 408
 
3.9%
ع 310
 
2.9%
ط 286
 
2.7%
ر 283
 
2.7%
Other values (17) 1806
17.2%
Common
ValueCountFrequency (%)
311
74.0%
4 81
 
19.3%
+ 28
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Arabic 10528
96.2%
ASCII 420
 
3.8%

Most frequent character per block

Arabic
ValueCountFrequency (%)
ا 2314
22.0%
ل 1566
14.9%
ي 1204
11.4%
ز 886
 
8.4%
و 771
 
7.3%
غ 694
 
6.6%
م 408
 
3.9%
ع 310
 
2.9%
ط 286
 
2.7%
ر 283
 
2.7%
Other values (17) 1806
17.2%
ASCII
ValueCountFrequency (%)
311
74.0%
4 81
 
19.3%
+ 28
 
6.7%

corporate
Text

Distinct55
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-10-24T09:38:06.199219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length35
Median length2
Mean length4.7024683
Min length2

Characters and Unicode

Total characters7049
Distinct characters73
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.3%

Sample

1st rowXe
2nd rowXe
3rd rowXe
4th rowXe
5th rowXe
ValueCountFrequency (%)
xe 1097
60.8%
jordan 96
 
5.3%
transports 93
 
5.2%
vestas 65
 
3.6%
gunsayil 27
 
1.5%
the 26
 
1.4%
save 23
 
1.3%
children 23
 
1.3%
support 22
 
1.2%
services 22
 
1.2%
Other values (77) 310
 
17.2%
2024-10-24T09:38:06.839844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1387
19.7%
X 1097
15.6%
r 464
 
6.6%
s 439
 
6.2%
a 394
 
5.6%
n 345
 
4.9%
o 324
 
4.6%
305
 
4.3%
t 257
 
3.6%
i 256
 
3.6%
Other values (63) 1781
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4780
67.8%
Uppercase Letter 1777
 
25.2%
Space Separator 305
 
4.3%
Other Letter 186
 
2.6%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1387
29.0%
r 464
 
9.7%
s 439
 
9.2%
a 394
 
8.2%
n 345
 
7.2%
o 324
 
6.8%
t 257
 
5.4%
i 256
 
5.4%
d 191
 
4.0%
p 150
 
3.1%
Other values (14) 573
12.0%
Uppercase Letter
ValueCountFrequency (%)
X 1097
61.7%
T 124
 
7.0%
S 101
 
5.7%
J 96
 
5.4%
V 86
 
4.8%
G 39
 
2.2%
C 34
 
1.9%
L 28
 
1.6%
A 27
 
1.5%
W 24
 
1.4%
Other values (14) 121
 
6.8%
Other Letter
ValueCountFrequency (%)
ه 29
15.6%
ا 23
12.4%
ك 20
10.8%
ل 20
10.8%
ي 17
9.1%
ر 14
7.5%
ن 12
6.5%
س 8
 
4.3%
م 8
 
4.3%
و 6
 
3.2%
Other values (13) 29
15.6%
Space Separator
ValueCountFrequency (%)
305
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6557
93.0%
Common 306
 
4.3%
Arabic 186
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1387
21.2%
X 1097
16.7%
r 464
 
7.1%
s 439
 
6.7%
a 394
 
6.0%
n 345
 
5.3%
o 324
 
4.9%
t 257
 
3.9%
i 256
 
3.9%
d 191
 
2.9%
Other values (38) 1403
21.4%
Arabic
ValueCountFrequency (%)
ه 29
15.6%
ا 23
12.4%
ك 20
10.8%
ل 20
10.8%
ي 17
9.1%
ر 14
7.5%
ن 12
6.5%
س 8
 
4.3%
م 8
 
4.3%
و 6
 
3.2%
Other values (13) 29
15.6%
Common
ValueCountFrequency (%)
305
99.7%
/ 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6863
97.4%
Arabic 186
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1387
20.2%
X 1097
16.0%
r 464
 
6.8%
s 439
 
6.4%
a 394
 
5.7%
n 345
 
5.0%
o 324
 
4.7%
305
 
4.4%
t 257
 
3.7%
i 256
 
3.7%
Other values (40) 1595
23.2%
Arabic
ValueCountFrequency (%)
ه 29
15.6%
ا 23
12.4%
ك 20
10.8%
ل 20
10.8%
ي 17
9.1%
ر 14
7.5%
ن 12
6.5%
س 8
 
4.3%
م 8
 
4.3%
و 6
 
3.2%
Other values (13) 29
15.6%

delivered by
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Mohamad Hane
290 
Amjad
223 
Dirar
196 
Mohamad J
172 
Mohamad Qasim
117 
Other values (25)
501 

Length

Max length13
Median length12
Mean length7.5923949
Min length3

Characters and Unicode

Total characters11381
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowOmar M
2nd rowMaen
3rd rowMohamad J
4th rowMohamad Qasim
5th rowMohamad Qasim

Common Values

ValueCountFrequency (%)
Mohamad Hane 290
19.3%
Amjad 223
14.9%
Dirar 196
13.1%
Mohamad J 172
11.5%
Mohamad Qasim 117
7.8%
Abdalla 80
 
5.3%
Khalil 63
 
4.2%
Gad 63
 
4.2%
Saddam 41
 
2.7%
Hamza 38
 
2.5%
Other values (20) 216
14.4%

Length

2024-10-24T09:38:07.111328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mohamad 579
27.5%
hane 290
13.8%
amjad 223
 
10.6%
dirar 196
 
9.3%
j 172
 
8.2%
qasim 120
 
5.7%
abdalla 80
 
3.8%
khalil 63
 
3.0%
gad 63
 
3.0%
saddam 41
 
1.9%
Other values (25) 276
13.1%

Most occurring characters

ValueCountFrequency (%)
a 2635
23.2%
d 1103
9.7%
m 1079
 
9.5%
h 715
 
6.3%
M 640
 
5.6%
o 616
 
5.4%
604
 
5.3%
r 468
 
4.1%
i 428
 
3.8%
A 368
 
3.2%
Other values (28) 2725
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8674
76.2%
Uppercase Letter 2103
 
18.5%
Space Separator 604
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2635
30.4%
d 1103
12.7%
m 1079
12.4%
h 715
 
8.2%
o 616
 
7.1%
r 468
 
5.4%
i 428
 
4.9%
e 359
 
4.1%
l 349
 
4.0%
n 319
 
3.7%
Other values (10) 603
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
M 640
30.4%
A 368
17.5%
H 328
15.6%
D 196
 
9.3%
J 186
 
8.8%
Q 120
 
5.7%
K 63
 
3.0%
G 63
 
3.0%
S 51
 
2.4%
T 33
 
1.6%
Other values (7) 55
 
2.6%
Space Separator
ValueCountFrequency (%)
604
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10777
94.7%
Common 604
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2635
24.5%
d 1103
10.2%
m 1079
10.0%
h 715
 
6.6%
M 640
 
5.9%
o 616
 
5.7%
r 468
 
4.3%
i 428
 
4.0%
A 368
 
3.4%
e 359
 
3.3%
Other values (27) 2366
22.0%
Common
ValueCountFrequency (%)
604
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2635
23.2%
d 1103
9.7%
m 1079
 
9.5%
h 715
 
6.3%
M 640
 
5.6%
o 616
 
5.4%
604
 
5.3%
r 468
 
4.1%
i 428
 
3.8%
A 368
 
3.2%
Other values (28) 2725
23.9%

returned by
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Mohammed Hane
290 
Amjad
223 
Dirar
196 
Mohamad J
170 
Gad
91 
Other values (25)
529 

Length

Max length13
Median length12
Mean length7.5096731
Min length3

Characters and Unicode

Total characters11257
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowOmar M
2nd rowMaen
3rd rowMohamad J
4th rowMohamad Qasim
5th rowOmar M

Common Values

ValueCountFrequency (%)
Mohammed Hane 290
19.3%
Amjad 223
14.9%
Dirar 196
13.1%
Mohamad J 170
11.3%
Gad 91
 
6.1%
Abdalla 81
 
5.4%
Mohamad Qasim 72
 
4.8%
Khalil 63
 
4.2%
Saddam 41
 
2.7%
Hamza 38
 
2.5%
Other values (20) 234
15.6%

Length

2024-10-24T09:38:07.329102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mohammed 290
14.0%
hane 290
14.0%
mohamad 242
11.7%
amjad 223
10.8%
dirar 196
9.5%
j 170
8.2%
gad 91
 
4.4%
abdalla 81
 
3.9%
qasim 75
 
3.6%
khalil 63
 
3.0%
Other values (26) 345
16.7%

Most occurring characters

ValueCountFrequency (%)
a 2254
20.0%
m 1289
11.5%
d 1087
9.7%
h 670
 
6.0%
e 655
 
5.8%
M 611
 
5.4%
o 571
 
5.1%
567
 
5.0%
r 478
 
4.2%
i 383
 
3.4%
Other values (28) 2692
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8624
76.6%
Uppercase Letter 2066
 
18.4%
Space Separator 567
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2254
26.1%
m 1289
14.9%
d 1087
12.6%
h 670
 
7.8%
e 655
 
7.6%
o 571
 
6.6%
r 478
 
5.5%
i 383
 
4.4%
l 351
 
4.1%
n 325
 
3.8%
Other values (10) 561
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
M 611
29.6%
A 369
17.9%
H 328
15.9%
D 196
 
9.5%
J 184
 
8.9%
G 91
 
4.4%
Q 75
 
3.6%
K 63
 
3.0%
S 51
 
2.5%
O 40
 
1.9%
Other values (7) 58
 
2.8%
Space Separator
ValueCountFrequency (%)
567
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10690
95.0%
Common 567
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2254
21.1%
m 1289
12.1%
d 1087
10.2%
h 670
 
6.3%
e 655
 
6.1%
M 611
 
5.7%
o 571
 
5.3%
r 478
 
4.5%
i 383
 
3.6%
A 369
 
3.5%
Other values (27) 2323
21.7%
Common
ValueCountFrequency (%)
567
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2254
20.0%
m 1289
11.5%
d 1087
9.7%
h 670
 
6.0%
e 655
 
5.8%
M 611
 
5.4%
o 571
 
5.1%
567
 
5.0%
r 478
 
4.2%
i 383
 
3.4%
Other values (28) 2692
23.9%

notes
Text

Distinct666
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-10-24T09:38:07.840820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length105
Median length62
Mean length17.46431
Min length3

Characters and Unicode

Total characters26179
Distinct characters47
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique533 ?
Unique (%)35.6%

Sample

1st rowاصلاح بودي ضربة باب خلفي شمال المركزية
2nd rowاصلاح بودي ضربة مرش يمين المركزية
3rd rowاصلاح حميان المركزية
4th rowغيار مراة كاملة شركة رنوت
5th rowغيار زيت + اصلاح مرشة الغزاوي/ المركزية
ValueCountFrequency (%)
غيار 825
 
16.8%
زيت 705
 
14.3%
231
 
4.7%
فلتر 193
 
3.9%
اصلاح 163
 
3.3%
بريك 162
 
3.3%
امامي 142
 
2.9%
خلفي 83
 
1.7%
القسطل 82
 
1.7%
كامل 81
 
1.6%
Other values (554) 2255
45.8%
2024-10-24T09:38:08.694336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3676
14.0%
ي 3339
12.8%
ا 3213
12.3%
ر 2110
 
8.1%
ت 1648
 
6.3%
ل 1235
 
4.7%
م 1028
 
3.9%
غ 1006
 
3.8%
ز 955
 
3.6%
+ 916
 
3.5%
Other values (37) 7053
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21418
81.8%
Space Separator 3676
 
14.0%
Math Symbol 916
 
3.5%
Other Punctuation 80
 
0.3%
Decimal Number 76
 
0.3%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ي 3339
15.6%
ا 3213
15.0%
ر 2110
9.9%
ت 1648
 
7.7%
ل 1235
 
5.8%
م 1028
 
4.8%
غ 1006
 
4.7%
ز 955
 
4.5%
ك 899
 
4.2%
ب 806
 
3.8%
Other values (23) 5179
24.2%
Decimal Number
ValueCountFrequency (%)
2 33
43.4%
4 29
38.2%
1 12
 
15.8%
5 1
 
1.3%
3 1
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
n 1
33.3%
r 1
33.3%
c 1
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 78
97.5%
, 2
 
2.5%
Space Separator
ValueCountFrequency (%)
3676
100.0%
Math Symbol
ValueCountFrequency (%)
+ 916
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Arabic 21418
81.8%
Common 4758
 
18.2%
Latin 3
 
< 0.1%

Most frequent character per script

Arabic
ValueCountFrequency (%)
ي 3339
15.6%
ا 3213
15.0%
ر 2110
9.9%
ت 1648
 
7.7%
ل 1235
 
5.8%
م 1028
 
4.8%
غ 1006
 
4.7%
ز 955
 
4.5%
ك 899
 
4.2%
ب 806
 
3.8%
Other values (23) 5179
24.2%
Common
ValueCountFrequency (%)
3676
77.3%
+ 916
 
19.3%
/ 78
 
1.6%
2 33
 
0.7%
4 29
 
0.6%
1 12
 
0.3%
( 5
 
0.1%
) 5
 
0.1%
, 2
 
< 0.1%
5 1
 
< 0.1%
Latin
ValueCountFrequency (%)
n 1
33.3%
r 1
33.3%
c 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Arabic 21418
81.8%
ASCII 4761
 
18.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3676
77.2%
+ 916
 
19.2%
/ 78
 
1.6%
2 33
 
0.7%
4 29
 
0.6%
1 12
 
0.3%
( 5
 
0.1%
) 5
 
0.1%
, 2
 
< 0.1%
5 1
 
< 0.1%
Other values (4) 4
 
0.1%
Arabic
ValueCountFrequency (%)
ي 3339
15.6%
ا 3213
15.0%
ر 2110
9.9%
ت 1648
 
7.7%
ل 1235
 
5.8%
م 1028
 
4.8%
غ 1006
 
4.7%
ز 955
 
4.5%
ك 899
 
4.2%
ب 806
 
3.8%
Other values (23) 5179
24.2%

Interactions

2024-10-24T09:37:53.202148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:36.070312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:38.543945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:40.971679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:43.734375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:45.979492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:48.312500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:50.657227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:53.455078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:36.394531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:38.810547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:41.251953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:44.066406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:46.265625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:48.564453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:50.934570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:53.714844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:36.699219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:39.139648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:41.522461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:44.341797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:46.559570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:48.853516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:51.206055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:53.961914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:37.061523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:39.412109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:41.778320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:44.607422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:46.924805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:49.120117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:51.857422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:54.216797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:37.384766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:39.748047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:42.040039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:44.882812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:47.210938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:49.483398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:52.132812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:54.490234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:37.668945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:40.082031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:42.633789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:45.165039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:47.529297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:49.886719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:52.411133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:54.781250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:37.944336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:40.380859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:42.883789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:45.432617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:47.781250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:50.131836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:52.664062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:55.179688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:38.253906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:40.702148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:43.245117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:45.716797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:48.055664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:50.406250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T09:37:52.950195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2024-10-24T09:38:09.329102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
#Fuel DiffFuel inFuel outKMs DiffKMs INKMs outcarcostdamage typedelivered bylocationreturned by
#1.000-0.0430.1730.166-0.0840.1430.1430.0730.1310.1080.4490.2720.446
Fuel Diff-0.0431.000-0.0960.1630.127-0.006-0.0060.0000.0640.0620.1690.1490.170
Fuel in0.173-0.0961.0000.948-0.010-0.010-0.0100.0000.0510.0550.2570.1970.261
Fuel out0.1660.1630.9481.0000.019-0.006-0.0060.0000.0780.0300.2480.1730.255
KMs Diff-0.0840.127-0.0100.0191.000-0.018-0.0180.0430.0600.0840.2080.1200.198
KMs IN0.143-0.006-0.010-0.006-0.0181.0001.0000.2280.0350.0330.0000.0600.000
KMs out0.143-0.006-0.010-0.006-0.0181.0001.0000.2280.0350.0330.0000.0600.000
car0.0730.0000.0000.0000.0430.2280.2281.0000.0000.0840.0310.0460.034
cost0.1310.0640.0510.0780.0600.0350.0350.0001.0000.2480.0410.3120.057
damage type0.1080.0620.0550.0300.0840.0330.0330.0840.2481.0000.2030.6600.217
delivered by0.4490.1690.2570.2480.2080.0000.0000.0310.0410.2031.0000.2240.974
location0.2720.1490.1970.1730.1200.0600.0600.0460.3120.6600.2241.0000.233
returned by0.4460.1700.2610.2550.1980.0000.0000.0340.0570.2170.9740.2331.000

Missing values

2024-10-24T09:37:55.603516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-24T09:37:56.405273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

#plate numbercardamage typedate inKMs INFuel indate readyKMs outKMs DiffFuel outFuel Diffcostlocationcorporatedelivered byreturned bynotes
0170-29280TUCSANاصلاح بودي2015-01-14230150.132015-01-1823030150.130.00150المركزيةXeOmar MOmar Mاصلاح بودي ضربة باب خلفي شمال المركزية
1270-26587ELANTRAاصلاح بودي2015-01-14436380.382015-01-184363800.380.00281المركزيةXeMaenMaenاصلاح بودي ضربة مرش يمين المركزية
2370-25180AVANZAاصلاح مكانيك2015-01-14398800.382015-01-243988990.750.3792المركزيةXeMohamad JMohamad Jاصلاح حميان المركزية
3470-26523FLUENCEاصلاح بودي2015-01-14437050.132015-01-1943725200.250.12250ابو خضرXeMohamad QasimMohamad Qasimغيار مراة كاملة شركة رنوت
4570-30719FLUENCEغيار زيت2015-01-14251450.132015-01-1925160150.130.00253المركزيةXeMohamad QasimOmar Mغيار زيت + اصلاح مرشة الغزاوي/ المركزية
5670-25207COROLLAاصلاح بودي2015-01-14853730.632015-01-2185383100.630.00650المركزيةشركه الهندسه الكهروميكانكيهMohamad QasimMohamad Qasimاصلاح ضربة امامية المركزية
6770-31356CAMRYاصلاح كوشوك2015-01-14317081.002015-01-1431719111.000.00320هانكونكVestasMohamad QasimMohamad Qasimغيار 4 كاوشوك هانكونك فيستاس
7870-24459FORTUNERغيار زيت2015-01-16694750.382015-01-166948380.500.1221الغزاويXeMohamad JMohamad Jغيار زيت
8916-94807PRADOغيار زيت2015-01-17251050.132015-01-1725121160.00-0.1321الغزاويXeMohamad QasimMaenغيار زيت
91070-24426FORTUNERغيار زيت2015-01-17575010.002015-01-1757512110.000.0021الغزاويXeMohamad QasimMohamad Qasimغيار زيت
#plate numbercardamage typedate inKMs INFuel indate readyKMs outKMs DiffFuel outFuel Diffcostlocationcorporatedelivered byreturned bynotes
1489149070-25363ELANTRAغيار زيت2016-02-011038050.502016-02-0110380940.500.0021الغزاوي+معاذXeAladdin RAladdin Rغيار زيت+ تشيك كامل
1490149170-26739CERATOاصلاح كوشوك2016-02-01919370.132016-02-0191962250.250.1215المركزيه+زكيXeAbdallaAbdallaجنط مضروب
1491149270-24540SPORTAGEغيار زيت2016-02-01805550.382016-02-018055940.380.0021كياXeAmjadAmjadغيار زيت+ تشيك كامل
1492149370-24127RIOاصلاح بودي2016-02-021150650.252016-02-0211507490.250.0020معاذ عليانXeTareqTareqغطاء بنزين
1493149439-50567HILUXاصلاح مكانيك2016-02-0295300.752016-02-029545150.750.0020المركزيةThe Risk Advisory GroupDirarDirarتشيك كامل +تقطيعه
1494149570-25213COROLLAاصلاح كوشوك2016-02-02841660.002016-02-028416930.000.00245ابو خضرXeMohamad QasimMohamad Qasimغيار زيت+ تشيك كامل+اطارات
1495149670-29981TUCSANغيار زيت2016-02-02601220.252016-02-026012640.250.0021هانكونكXeAmjadAmjadغيار زيت+ تشيك كامل
1496149770-29538COROLLAاصلاح بودي2016-01-27681490.132016-02-036815450.250.12200الغزاويXeTareqTareqضربة جناح خلفي يمين
1497149870-35613CAMRYاصلاح بودي2016-01-31437160.252016-02-034372480.380.1380اليادودةXeTareqTareqطبون خلفي
1498149970-34029FORTUNERغيار زيت2016-02-03301280.382016-02-033013680.380.0021المكتبSave The ChildrenTareqTareqغيار زيت+ تشيك كامل